Automatic Keyphrase Extraction from Medical Documents

Authors:
Kamal Sarkar
Affiliations:
Computer Science & Engineering Department, Jadavpur University, Kolkata, India 700 032
Venue:
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Year:
2009

Citing 7
Cited 0

Phrasier: a system for interactive document retrieval using keyphrases

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Improving browsing in digital libraries with keyphrase indexes

Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
Automatic extraction of document keyphrases for use in digital libraries: evaluation and applications

Journal of the American Society for Information Science and Technology
Learning Algorithms for Keyphrase Extraction

Information Retrieval
Document keyphrases as subject metadata: incorporating document key concepts in search results

Information Retrieval
Domain-specific keyphrase extraction

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Developing a robust part-of-speech tagger for biomedical text

PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Keyphrases provide semantic metadata that summarizes the documents and enable the reader to quickly determine whether the given article is in the reader's fields of interest. This paper presents an automatic keyphrase extraction method based on the naive Bayesian learning that exploits a number of domain-specific features to boost up the keyphrase extraction performance in medical domain. The proposed method has been compared to a popular keyphrase extraction algorithm, called Kea.